Elizabeth Valles
23 Nov 2019
%>% - the pipe operator, similar to | in Linuxgather - push data that is currently in columns into rowsspread - pull the values into their own columnsselect - choose columnsfilter - choose rows based on conditionsarrange - sort rows based on column valuesmutate - convert data in an existing column into new data in a new columnsummarize - provide summary data for a columngroup_by - group data based on a variable; often used with summarizelibrary(tidyverse)
str(billboard) # the base R way to see the structure of a dataframe## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 317 obs. of 79 variables:
## $ artist : chr "2 Pac" "2Ge+her" "3 Doors Down" "3 Doors Down" ...
## $ track : chr "Baby Don't Cry (Keep..." "The Hardest Part Of ..." "Kryptonite" "Loser" ...
## $ date.entered: Date, format: "2000-02-26" "2000-09-02" ...
## $ wk1 : num 87 91 81 76 57 51 97 84 59 76 ...
## $ wk2 : num 82 87 70 76 34 39 97 62 53 76 ...
## $ wk3 : num 72 92 68 72 25 34 96 51 38 74 ...
## $ wk4 : num 77 NA 67 69 17 26 95 41 28 69 ...
## $ wk5 : num 87 NA 66 67 17 26 100 38 21 68 ...
## $ wk6 : num 94 NA 57 65 31 19 NA 35 18 67 ...
## $ wk7 : num 99 NA 54 55 36 2 NA 35 16 61 ...
## $ wk8 : num NA NA 53 59 49 2 NA 38 14 58 ...
## $ wk9 : num NA NA 51 62 53 3 NA 38 12 57 ...
## $ wk10 : num NA NA 51 61 57 6 NA 36 10 59 ...
## $ wk11 : num NA NA 51 61 64 7 NA 37 9 66 ...
## $ wk12 : num NA NA 51 59 70 22 NA 37 8 68 ...
## $ wk13 : num NA NA 47 61 75 29 NA 38 6 61 ...
## $ wk14 : num NA NA 44 66 76 36 NA 49 1 67 ...
## $ wk15 : num NA NA 38 72 78 47 NA 61 2 59 ...
## $ wk16 : num NA NA 28 76 85 67 NA 63 2 63 ...
## $ wk17 : num NA NA 22 75 92 66 NA 62 2 67 ...
## $ wk18 : num NA NA 18 67 96 84 NA 67 2 71 ...
## $ wk19 : num NA NA 18 73 NA 93 NA 83 3 79 ...
## $ wk20 : num NA NA 14 70 NA 94 NA 86 4 89 ...
## $ wk21 : num NA NA 12 NA NA NA NA NA 5 NA ...
## $ wk22 : num NA NA 7 NA NA NA NA NA 5 NA ...
## $ wk23 : num NA NA 6 NA NA NA NA NA 6 NA ...
## $ wk24 : num NA NA 6 NA NA NA NA NA 9 NA ...
## $ wk25 : num NA NA 6 NA NA NA NA NA 13 NA ...
## $ wk26 : num NA NA 5 NA NA NA NA NA 14 NA ...
## $ wk27 : num NA NA 5 NA NA NA NA NA 16 NA ...
## $ wk28 : num NA NA 4 NA NA NA NA NA 23 NA ...
## $ wk29 : num NA NA 4 NA NA NA NA NA 22 NA ...
## $ wk30 : num NA NA 4 NA NA NA NA NA 33 NA ...
## $ wk31 : num NA NA 4 NA NA NA NA NA 36 NA ...
## $ wk32 : num NA NA 3 NA NA NA NA NA 43 NA ...
## $ wk33 : num NA NA 3 NA NA NA NA NA NA NA ...
## $ wk34 : num NA NA 3 NA NA NA NA NA NA NA ...
## $ wk35 : num NA NA 4 NA NA NA NA NA NA NA ...
## $ wk36 : num NA NA 5 NA NA NA NA NA NA NA ...
## $ wk37 : num NA NA 5 NA NA NA NA NA NA NA ...
## $ wk38 : num NA NA 9 NA NA NA NA NA NA NA ...
## $ wk39 : num NA NA 9 NA NA NA NA NA NA NA ...
## $ wk40 : num NA NA 15 NA NA NA NA NA NA NA ...
## $ wk41 : num NA NA 14 NA NA NA NA NA NA NA ...
## $ wk42 : num NA NA 13 NA NA NA NA NA NA NA ...
## $ wk43 : num NA NA 14 NA NA NA NA NA NA NA ...
## $ wk44 : num NA NA 16 NA NA NA NA NA NA NA ...
## $ wk45 : num NA NA 17 NA NA NA NA NA NA NA ...
## $ wk46 : num NA NA 21 NA NA NA NA NA NA NA ...
## $ wk47 : num NA NA 22 NA NA NA NA NA NA NA ...
## $ wk48 : num NA NA 24 NA NA NA NA NA NA NA ...
## $ wk49 : num NA NA 28 NA NA NA NA NA NA NA ...
## $ wk50 : num NA NA 33 NA NA NA NA NA NA NA ...
## $ wk51 : num NA NA 42 NA NA NA NA NA NA NA ...
## $ wk52 : num NA NA 42 NA NA NA NA NA NA NA ...
## $ wk53 : num NA NA 49 NA NA NA NA NA NA NA ...
## $ wk54 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk55 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk56 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk57 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk58 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk59 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk60 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk61 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk62 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk63 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk64 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk65 : num NA NA NA NA NA NA NA NA NA NA ...
## $ wk66 : logi NA NA NA NA NA NA ...
## $ wk67 : logi NA NA NA NA NA NA ...
## $ wk68 : logi NA NA NA NA NA NA ...
## $ wk69 : logi NA NA NA NA NA NA ...
## $ wk70 : logi NA NA NA NA NA NA ...
## $ wk71 : logi NA NA NA NA NA NA ...
## $ wk72 : logi NA NA NA NA NA NA ...
## $ wk73 : logi NA NA NA NA NA NA ...
## $ wk74 : logi NA NA NA NA NA NA ...
## $ wk75 : logi NA NA NA NA NA NA ...
## $ wk76 : logi NA NA NA NA NA NA ...
## - attr(*, "spec")=
## .. cols(
## .. year = col_skip(),
## .. artist = col_character(),
## .. track = col_character(),
## .. time = col_skip(),
## .. date.entered = col_date(format = ""),
## .. wk1 = col_double(),
## .. wk2 = col_double(),
## .. wk3 = col_double(),
## .. wk4 = col_double(),
## .. wk5 = col_double(),
## .. wk6 = col_double(),
## .. wk7 = col_double(),
## .. wk8 = col_double(),
## .. wk9 = col_double(),
## .. wk10 = col_double(),
## .. wk11 = col_double(),
## .. wk12 = col_double(),
## .. wk13 = col_double(),
## .. wk14 = col_double(),
## .. wk15 = col_double(),
## .. wk16 = col_double(),
## .. wk17 = col_double(),
## .. wk18 = col_double(),
## .. wk19 = col_double(),
## .. wk20 = col_double(),
## .. wk21 = col_double(),
## .. wk22 = col_double(),
## .. wk23 = col_double(),
## .. wk24 = col_double(),
## .. wk25 = col_double(),
## .. wk26 = col_double(),
## .. wk27 = col_double(),
## .. wk28 = col_double(),
## .. wk29 = col_double(),
## .. wk30 = col_double(),
## .. wk31 = col_double(),
## .. wk32 = col_double(),
## .. wk33 = col_double(),
## .. wk34 = col_double(),
## .. wk35 = col_double(),
## .. wk36 = col_double(),
## .. wk37 = col_double(),
## .. wk38 = col_double(),
## .. wk39 = col_double(),
## .. wk40 = col_double(),
## .. wk41 = col_double(),
## .. wk42 = col_double(),
## .. wk43 = col_double(),
## .. wk44 = col_double(),
## .. wk45 = col_double(),
## .. wk46 = col_double(),
## .. wk47 = col_double(),
## .. wk48 = col_double(),
## .. wk49 = col_double(),
## .. wk50 = col_double(),
## .. wk51 = col_double(),
## .. wk52 = col_double(),
## .. wk53 = col_double(),
## .. wk54 = col_double(),
## .. wk55 = col_double(),
## .. wk56 = col_double(),
## .. wk57 = col_double(),
## .. wk58 = col_double(),
## .. wk59 = col_double(),
## .. wk60 = col_double(),
## .. wk61 = col_double(),
## .. wk62 = col_double(),
## .. wk63 = col_double(),
## .. wk64 = col_double(),
## .. wk65 = col_double(),
## .. wk66 = col_logical(),
## .. wk67 = col_logical(),
## .. wk68 = col_logical(),
## .. wk69 = col_logical(),
## .. wk70 = col_logical(),
## .. wk71 = col_logical(),
## .. wk72 = col_logical(),
## .. wk73 = col_logical(),
## .. wk74 = col_logical(),
## .. wk75 = col_logical(),
## .. wk76 = col_logical()
## .. )head(billboard)## # A tibble: 6 x 79
## artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
## <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
## 2 2Ge+h… The … 2000-09-02 91 87 92 NA NA NA NA NA
## 3 3 Doo… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
## 4 3 Doo… Loser 2000-10-21 76 76 72 69 67 65 55 59
## 5 504 B… Wobb… 2000-04-15 57 34 25 17 17 31 36 49
## 6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
## # … with 68 more variables: wk9 <dbl>, wk10 <dbl>, wk11 <dbl>, wk12 <dbl>,
## # wk13 <dbl>, wk14 <dbl>, wk15 <dbl>, wk16 <dbl>, wk17 <dbl>,
## # wk18 <dbl>, wk19 <dbl>, wk20 <dbl>, wk21 <dbl>, wk22 <dbl>,
## # wk23 <dbl>, wk24 <dbl>, wk25 <dbl>, wk26 <dbl>, wk27 <dbl>,
## # wk28 <dbl>, wk29 <dbl>, wk30 <dbl>, wk31 <dbl>, wk32 <dbl>,
## # wk33 <dbl>, wk34 <dbl>, wk35 <dbl>, wk36 <dbl>, wk37 <dbl>,
## # wk38 <dbl>, wk39 <dbl>, wk40 <dbl>, wk41 <dbl>, wk42 <dbl>,
## # wk43 <dbl>, wk44 <dbl>, wk45 <dbl>, wk46 <dbl>, wk47 <dbl>,
## # wk48 <dbl>, wk49 <dbl>, wk50 <dbl>, wk51 <dbl>, wk52 <dbl>,
## # wk53 <dbl>, wk54 <dbl>, wk55 <dbl>, wk56 <dbl>, wk57 <dbl>,
## # wk58 <dbl>, wk59 <dbl>, wk60 <dbl>, wk61 <dbl>, wk62 <dbl>,
## # wk63 <dbl>, wk64 <dbl>, wk65 <dbl>, wk66 <lgl>, wk67 <lgl>,
## # wk68 <lgl>, wk69 <lgl>, wk70 <lgl>, wk71 <lgl>, wk72 <lgl>,
## # wk73 <lgl>, wk74 <lgl>, wk75 <lgl>, wk76 <lgl>billboard %>%
gather(wk1:wk76, key = "week", value = "rank") # combine 76 columns into 2## # A tibble: 24,092 x 5
## artist track date.entered week rank
## <chr> <chr> <date> <chr> <dbl>
## 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
## 2 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
## 3 3 Doors Down Kryptonite 2000-04-08 wk1 81
## 4 3 Doors Down Loser 2000-10-21 wk1 76
## 5 504 Boyz Wobble Wobble 2000-04-15 wk1 57
## 6 98^0 Give Me Just One Nig... 2000-08-19 wk1 51
## 7 A*Teens Dancing Queen 2000-07-08 wk1 97
## 8 Aaliyah I Don't Wanna 2000-01-29 wk1 84
## 9 Aaliyah Try Again 2000-03-18 wk1 59
## 10 Adams, Yolanda Open My Heart 2000-08-26 wk1 76
## # … with 24,082 more rowsbillboard %>%
gather(wk1:wk76, key = "week", value = "rank") %>% # combine 76 columns into 2
filter(!is.na(rank)) # remove rows with NA## # A tibble: 5,307 x 5
## artist track date.entered week rank
## <chr> <chr> <date> <chr> <dbl>
## 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
## 2 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91
## 3 3 Doors Down Kryptonite 2000-04-08 wk1 81
## 4 3 Doors Down Loser 2000-10-21 wk1 76
## 5 504 Boyz Wobble Wobble 2000-04-15 wk1 57
## 6 98^0 Give Me Just One Nig... 2000-08-19 wk1 51
## 7 A*Teens Dancing Queen 2000-07-08 wk1 97
## 8 Aaliyah I Don't Wanna 2000-01-29 wk1 84
## 9 Aaliyah Try Again 2000-03-18 wk1 59
## 10 Adams, Yolanda Open My Heart 2000-08-26 wk1 76
## # … with 5,297 more rowsbillboard %>%
gather(wk1:wk76, key = "week", value = "rank") %>% # combine 76 columns into 2
filter(!is.na(rank)) %>% # remove rows with NA
mutate(weeks = as.numeric(str_extract(week, "[:digit:]+"))) # change "wk1" to 1, etc.## # A tibble: 5,307 x 6
## artist track date.entered week rank weeks
## <chr> <chr> <date> <chr> <dbl> <dbl>
## 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87 1
## 2 2Ge+her The Hardest Part Of ... 2000-09-02 wk1 91 1
## 3 3 Doors Down Kryptonite 2000-04-08 wk1 81 1
## 4 3 Doors Down Loser 2000-10-21 wk1 76 1
## 5 504 Boyz Wobble Wobble 2000-04-15 wk1 57 1
## 6 98^0 Give Me Just One Nig... 2000-08-19 wk1 51 1
## 7 A*Teens Dancing Queen 2000-07-08 wk1 97 1
## 8 Aaliyah I Don't Wanna 2000-01-29 wk1 84 1
## 9 Aaliyah Try Again 2000-03-18 wk1 59 1
## 10 Adams, Yolanda Open My Heart 2000-08-26 wk1 76 1
## # … with 5,297 more rowsbillboard %>%
gather(wk1:wk76, key = "week", value = "rank") %>% # combine 76 columns into 2
filter(!is.na(rank)) %>% # remove rows with NA
mutate(weeks = as.numeric(str_extract(week, "[:digit:]+"))) %>% # change "wk1" to 1, etc.
mutate(date.out = date.entered + (weeks - 1) * 7) # create a new variable## # A tibble: 5,307 x 7
## artist track date.entered week rank weeks date.out
## <chr> <chr> <date> <chr> <dbl> <dbl> <date>
## 1 2 Pac Baby Don't Cry (… 2000-02-26 wk1 87 1 2000-02-26
## 2 2Ge+her The Hardest Part… 2000-09-02 wk1 91 1 2000-09-02
## 3 3 Doors Down Kryptonite 2000-04-08 wk1 81 1 2000-04-08
## 4 3 Doors Down Loser 2000-10-21 wk1 76 1 2000-10-21
## 5 504 Boyz Wobble Wobble 2000-04-15 wk1 57 1 2000-04-15
## 6 98^0 Give Me Just One… 2000-08-19 wk1 51 1 2000-08-19
## 7 A*Teens Dancing Queen 2000-07-08 wk1 97 1 2000-07-08
## 8 Aaliyah I Don't Wanna 2000-01-29 wk1 84 1 2000-01-29
## 9 Aaliyah Try Again 2000-03-18 wk1 59 1 2000-03-18
## 10 Adams, Yola… Open My Heart 2000-08-26 wk1 76 1 2000-08-26
## # … with 5,297 more rowsbillboard %>%
gather(wk1:wk76, key = "week", value = "rank") %>% # combine 76 columns into 2
filter(!is.na(rank)) %>% # remove rows with NA
mutate(weeks = as.numeric(str_extract(week, "[:digit:]+"))) %>% # change "wk1" to 1, etc.
mutate(date.out = date.entered + (weeks - 1) * 7) %>% # create a new variable
select(rank, date.entered, weeks, date.out, track, artist) # reorder the columnsax## # A tibble: 5,307 x 6
## rank date.entered weeks date.out track artist
## <dbl> <date> <dbl> <date> <chr> <chr>
## 1 87 2000-02-26 1 2000-02-26 Baby Don't Cry (Keep.… 2 Pac
## 2 91 2000-09-02 1 2000-09-02 The Hardest Part Of .… 2Ge+her
## 3 81 2000-04-08 1 2000-04-08 Kryptonite 3 Doors Down
## 4 76 2000-10-21 1 2000-10-21 Loser 3 Doors Down
## 5 57 2000-04-15 1 2000-04-15 Wobble Wobble 504 Boyz
## 6 51 2000-08-19 1 2000-08-19 Give Me Just One Nig.… 98^0
## 7 97 2000-07-08 1 2000-07-08 Dancing Queen A*Teens
## 8 84 2000-01-29 1 2000-01-29 I Don't Wanna Aaliyah
## 9 59 2000-03-18 1 2000-03-18 Try Again Aaliyah
## 10 76 2000-08-26 1 2000-08-26 Open My Heart Adams, Yolan…
## # … with 5,297 more rowsbillboard %>%
gather(wk1:wk76, key = "week", value = "rank") %>% # combine 76 columns into 2
filter(!is.na(rank)) %>% # remove rows with NA
mutate(weeks = as.numeric(str_extract(week, "[:digit:]+"))) %>% # change "wk1" to 1, etc.
mutate(date.out = date.entered + (weeks - 1) * 7) %>% # create a new variable
select(rank, date.entered, weeks, date.out, track, artist) %>% # reorder the columnsax
arrange(rank, date.out) # sort by rank and date## # A tibble: 5,307 x 6
## rank date.entered weeks date.out track artist
## <dbl> <date> <dbl> <date> <chr> <chr>
## 1 1 1999-11-27 8 2000-01-15 What A Girl Wants Aguilera, Chris…
## 2 1 1999-11-27 9 2000-01-22 What A Girl Wants Aguilera, Chris…
## 3 1 1999-10-23 15 2000-01-29 I Knew I Loved You Savage Garden
## 4 1 1999-10-23 16 2000-02-05 I Knew I Loved You Savage Garden
## 5 1 1999-10-23 17 2000-02-12 I Knew I Loved You Savage Garden
## 6 1 1999-12-11 11 2000-02-19 Thank God I Found … Carey, Mariah
## 7 1 1999-10-23 19 2000-02-26 I Knew I Loved You Savage Garden
## 8 1 1999-06-05 40 2000-03-04 Amazed Lonestar
## 9 1 1999-06-05 41 2000-03-11 Amazed Lonestar
## 10 1 1999-12-25 13 2000-03-18 Say My Name Destiny's Child
## # … with 5,297 more rowsbillboard %>%
gather(wk1:wk76, key = "week", value = "rank") %>% # combine 76 columns into 2
filter(!is.na(rank)) %>% # remove rows with NA
mutate(weeks = as.numeric(str_extract(week, "[:digit:]+"))) %>% # change "wk1" to 1, etc.
mutate(date.out = date.entered + (weeks - 1) * 7) %>% # create a new variable
select(rank, date.entered, weeks, date.out, track, artist) %>% # reorder the columnsax
arrange(rank, date.out) # sort by rank and date## # A tibble: 5,307 x 6
## rank date.entered weeks date.out track artist
## <dbl> <date> <dbl> <date> <chr> <chr>
## 1 1 1999-11-27 8 2000-01-15 What A Girl Wants Aguilera, Chris…
## 2 1 1999-11-27 9 2000-01-22 What A Girl Wants Aguilera, Chris…
## 3 1 1999-10-23 15 2000-01-29 I Knew I Loved You Savage Garden
## 4 1 1999-10-23 16 2000-02-05 I Knew I Loved You Savage Garden
## 5 1 1999-10-23 17 2000-02-12 I Knew I Loved You Savage Garden
## 6 1 1999-12-11 11 2000-02-19 Thank God I Found … Carey, Mariah
## 7 1 1999-10-23 19 2000-02-26 I Knew I Loved You Savage Garden
## 8 1 1999-06-05 40 2000-03-04 Amazed Lonestar
## 9 1 1999-06-05 41 2000-03-11 Amazed Lonestar
## 10 1 1999-12-25 13 2000-03-18 Say My Name Destiny's Child
## # … with 5,297 more rowsLas reglas gramaticales de los gráficos en ocasiones son matemáticas y otras estéticas.
Las matemática proporcionan herramientas simbólicas para representar abstracciones.
La estética, en el sentido griego original, ofrece principios para relacionar los atributos sensoriales (color, forma, sonido, etc.) con las abstracciones.
Wilkinson, 2005
#install.packages("tidyverse")
#install.packages("ggplot2")
#devtools::install_github("tidyverse/ggplot2")
library(tidyverse)
library(ggplot2)
library(plotly)
library(yaml)capitulos_rladies <- readr::read_csv("https://raw.githubusercontent.com/cienciadedatos/datos-de-miercoles/master/datos/2019/2019-06-26/capitulos_rladies.csv")
glimpse(capitulos_rladies)## Observations: 160
## Variables: 7
## $ capitulo <chr> "R-Ladies Barcelona", "R-Ladies Ushuaia", "R-Ladies Bil…
## $ creacion <dttm> 2016-10-22 10:56:36, 2018-05-09 16:39:48, 2019-02-27 0…
## $ miembros <dbl> 389, 23, 38, 114, 1213, 46, 261, 30, 108, 292, 149, 658…
## $ latitud <dbl> 41.40, -54.79, 43.25, -41.14, -37.81, 45.25, -30.04, 40…
## $ longitud <dbl> 2.17, -68.31, -2.93, -71.32, 144.96, 19.85, -51.22, -74…
## $ ciudad <chr> "Barcelona", "Ushuaia", "Bilbao", "San Carlos de Barilo…
## $ pais <chr> "ES", "AR", "ES", "AR", "AU", "RS", "BR", "US", "AU", "…discretas, continuas…
una, dos variables, más…
ggplot(data,mapping=aes())
cap <- ggplot(data = capitulos_rladies, mapping = aes(pais, miembros))
cap
geom_“gráfico”
cap +
geom_bar(stat = "identity")
cap + geom_bar(stat = "identity") +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) 
cap +
geom_bar(stat = "identity",aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE)
cap +
geom_bar(stat = "identity",aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE) + scale_fill_hue(l=40, c=60)
cap +
geom_bar(stat = "identity",aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE) + scale_fill_grey(start = 0.1, end = 0.9)
cap + geom_bar(stat = "identity", aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE) + scale_fill_hue(l=40, c=60) +
ggtitle("Miembros de R-Ladies por país ") + xlab ("país")
cap + geom_bar(stat = "identity", aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE) + scale_fill_hue(l=40, c=60) +
ggtitle("Miembros de R-Ladies por país ") + xlab ("país") +
coord_flip()
bar <- cap + geom_bar(stat = "identity", aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE) + scale_fill_hue(l=40, c=60) +
ggtitle("Miembros de R-Ladies por país ") + xlab ("país")
ggplotly(bar)pai <- ggplot(capitulos_rladies, aes(creacion, miembros)) + geom_point(aes(color = capitulo)) +
scale_fill_hue(l=20, c=100) + facet_wrap(~pais) +
ggtitle("Miembros de R-Ladies por país y capítulo") + xlab ("país") +
ylab ("miembros") + theme(axis.text.x = element_text(5), text = element_text(size = 8)) +
guides (color = FALSE)yea <- capitulos_rladies %>%
separate(creacion, sep=c("-"), into = c("year", "month", "day")) %>%
ggplot(aes(month, miembros)) +
geom_point(aes(color = pais)) +
facet_wrap(~year) +
scale_fill_hue(l=20, c=100) +
ggtitle("Miembros de R-Ladies por fecha de creación") +
xlab ("país") +
ylab ("miembros") +
theme(axis.text.x = element_text(5), text = element_text(size = 12))
yea.plotly <- ggplotly(yea)bar <- cap + geom_bar(stat = "identity", aes(fill = capitulo)) +
theme(axis.text.x = element_text(size = 5), text = element_text(12)) +
guides(fill = FALSE) + scale_fill_hue(l=40, c=60) +
ggtitle("Miembros de R-Ladies por país ") + xlab ("país")
ggsave("bar.png", width = 12, height = 10)htmlwidgets::saveWidget(yea.plotly, "yea.ploy.html")eventos_rladies <- readr::read_csv("https://raw.githubusercontent.com/cienciadedatos/datos-de-miercoles/master/datos/2019/2019-06-26/eventos_rladies.csv")
glimpse(eventos_rladies)## Observations: 1,534
## Variables: 6
## $ capitulo <chr> "R-Ladies Barcelona", "R-Ladies Barcelona", "…
## $ titulo_evento <chr> "¡Primer evento de R-Ladies Barcelona!", "Sca…
## $ fecha_local <date> 2016-11-21, 2016-12-05, 2017-01-16, 2017-02-…
## $ hora_local <time> 19:00:00, 19:00:00, 19:00:00, 19:00:00, 19:0…
## $ respuesta_asistire <dbl> 18, 58, 33, 38, 16, 31, 18, 1, 33, 36, 28, 30…
## $ descripcion_evento <chr> "<p>Estamos preparando el primer encuentro de…rladies <- full_join(capitulos_rladies, eventos_rladies, by = "capitulo")latam <- c("MX", "BZ", "GT", "HN", "SV", "NI", "CR", "PA", "CO", "VE", "EC", "BO", "PE", "CL", "AR", "BR", "UY", "PY", "SR", "GY")
pr <- rladies %>%
filter(pais %in% latam) %>%
ggplot(aes(fecha_local, respuesta_asistire)) +
geom_point(aes(color = ciudad)) +
geom_line(aes(color = pais)) +
facet_wrap(~capitulo) +
scale_fill_hue(l=20, c=100) +
guides (color = FALSE) +
ggtitle("Reuniones R-ladies por año") +
xlab ("mes") +
ylab ("asistentes") +
theme(axis.text.x = element_text(3), text = element_text(size = 8))